Sequence-of-sequences model for 3d object recognition

ABSTRACT

Systems and methods are disclosed for capturing multiple sequences of views of a three-dimensional object using a plurality of virtual cameras. The systems and methods generate aligned sequences from the multiple sequences based on an arrangement of the plurality of virtual cameras in relation to the three-dimensional object. Using a convolutional network, the systems and methods classify the three-dimensional object based on the aligned sequences and identify the three-dimensional object using the classification.

PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/878,591, filed on Aug. 1, 2022, which is a continuation of U.S.patent application Ser. No. 16/870,138, filed on May 8, 2020, now issuedas U.S. Pat. No. 11,410,439, which claims the benefit of priority toU.S. Provisional Patent Application Ser. No. 62/845,471, filed May 9,2019 which are incorporated by reference herein in their entireties.

TECHNICAL FIELD

Embodiments of the present disclosure generally relate to objectrecognition. More particularly, but not by way of limitation, thepresent disclosure addresses systems and methods for asequence-of-sequences model for three-dimensional object recognition.

BACKGROUND

Three-dimensional object recognition methods based on deep learning canbe classified into three categories: multi-view methods, volumetricmethods and point-cloud methods. Volumetric methods naturally extend thetwo-dimensional (2D) convolutional neural network (CNN) to a 3D CNN toprocess the input 3D volume. Point-cloud methods represent each point ofa 3D model in a discrete way.

Multi-view methods project a 3D object into multiple views of differentviewpoints. The projected views are fed into a convolutional neuralnetwork (CNN) to obtain view-wise features. Since multi-view methodsonly rely on a traditional CNN, it feasibly supports fine-tuning onoff-the-shelf models pre-trained on a large-scale image dataset.

Another approach to multi-view methods is a multi-view convolutionalneural network, which pools the views' features through max-pooling. Theelement-wise max pooling operation retains the activation from one ofthe views and disregards others.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some embodiments are illustratedby way of example, and not limitation, in the figures of theaccompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome examples.

FIG. 6 is a block diagram illustrating an object recognition system,according to some example embodiments.

FIG. 7 illustrates a machine learning model, according to some exampleembodiments.

FIG. 8 illustrates a sequence model, according to some exampleembodiments.

FIG. 9 illustrates a sequence-of-sequences model, according to someexample embodiments.

FIG. 10 illustrates virtual camera positions in a sequence-of-sequencesmodel, according to some example embodiments

FIG. 11 is a flow diagram of a method for identifying athree-dimensional model using a sequence-of-sequences model, accordingto some example embodiments.

FIG. 12 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 13 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present subject matter. Thus, the appearances of the phrase “inone embodiment” or “in an embodiment” appearing in various placesthroughout the specification are not necessarily all referring to thesame embodiment.

For purposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the presentsubject matter. However, it will be apparent to one of ordinary skill inthe art that embodiments of the subject matter described may bepracticed without the specific details presented herein, or in variouscombinations, as described herein. Furthermore, well-known features maybe omitted or simplified in order not to obscure the describedembodiments. Various examples may be given throughout this description.These are merely descriptions of specific embodiments. The scope ormeaning of the claims is not limited to the examples given.

The present disclosure addresses the 3D object recognition problemthrough modeling the sequence of views. In addition to exploiting thevisual appearance provided in the multiple individual project views, theproposed methods and systems further exploits the order of views alongthe sequence into consideration. The proposed sequence of sequencesmodel therefore not only characterizes the order within each sequencebut also models the order among sequences.

The object recognition system captures multiple sequences of views of a3D object using virtual cameras. Each sequence of the multiple sequencesrepresents a unique order of views of the 3D object. The objectrecognition system generates an alignment function based on thedifferent starting views within the multiple sequences of views. Thestarting view is the first view within each unique order of view. Theobject recognition system aligns the multiple sequences of views usingthe alignment function to align the multiple sequences of views to acanonical viewpoint. Thus, aligned sequences are generated using thealignment function. The aligned sequences are generated from themultiple sequences based on an arrangement of the virtual cameras inrelation to the 3D object. In some examples, the aligned sequences areconcatenated into a single vector to generate a concatenated alignedsequence.

Using a convolutional neural network, the object recognition systemclassifies the 3D object based on the aligned sequences. The objectrecognition system, in some examples, classifies the 3D object based onthe concatenated aligned sequence. The object recognition systemidentifies the 3D object using the classification.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program productsillustrative of embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104. Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

A messaging client 104 is able to communicate and exchange data withanother messaging client 104 and with the messaging server system 108via the network 106. The data exchanged between messaging client 104,and between a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, application servers 112. Theapplication servers 112 are communicatively coupled to a database server118, which facilitates access to a database 120 that stores dataassociated with messages processed by the application servers 112.Similarly, a web server 126 is coupled to the application servers 112,and provides web-based interfaces to the application servers 112. Tothis end, the web server 126 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 112. The Application Program Interface (API) server110 exposes various functions supported by the application servers 112,including account registration, login functionality, the sending ofmessages, via the application servers 112, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 114, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 112 host a number of server applications andsubsystems, including for example a messaging server 114, an imageprocessing server 116, and a social network server 122. The messagingserver 114 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor and memory intensive processing ofdata may also be performed server-side by the messaging server 114, inview of the hardware requirements for such processing.

The application servers 112 also include an image processing server 116that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 114.

The social network server 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 114. To this end, the social network server 122maintains and accesses an entity graph 306 (as shown in FIG. 3 ) withinthe database 120. Examples of functions and services supported by thesocial network server 122 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

The object recognition system 124 may include one or more servers andmay be associated with a cloud-based application. The object recognitionsystem 124 obtains three-dimensional (3D) object data from a database120. The object recognition system 124 analyzes 3D objects andautomatically identifies the 3D objects based on their classifications.The details of the object recognition system 124 are provided below inconnection with FIGS. 6-11 .

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 112. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the sever-side by the application servers 112. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 206, a mapsystem 208, a game system 210, and an object recognition system 124.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 114. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 212 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface212 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 206 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system206 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 206 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 206 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text or image that can be overlaid on top of aphotograph taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the augmentation system 206uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 120 and accessed through the database server 118.

In some examples, the augmentation system 206 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 206 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 206 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 206 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time.

The map system 208 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 208 enables thedisplay of user icons or avatars (e.g., stored in profile data 308) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 210 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games that can be launched by auser within the context of the messaging client 104, and played withother users of the messaging system 100. The messaging system 100further enables a particular user to invite other users to participatein the play of a specific game, by issuing invitations to such otherusers from the messaging client 104. The messaging client 104 alsosupports both the voice and text messaging (e.g., chats) within thecontext of gameplay, provides a leaderboard for the games, and alsosupports the provision of in-game rewards (e.g., coins and items).

The object recognition system 124 obtains 3D object data andautomatically identifies the 3D object based on its classification. Insome examples, the object recognition system 124 may be supported by themessaging client 104 or the application servers 112.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 120 of the messaging server system 108,according to certain examples. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302 is described below with reference to FIG. 4 .

An entity table 304 stores entity data, and is linked (e.g.,referentially) to an entity graph 306 and profile data 308. Entities forwhich records are maintained within the entity table 304 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 306 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 308 stores multiple types of profile data about aparticular entity. The profile data 308 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 308 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 308 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 120 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 316) and images (for which data is stored in an image table318).

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 318includes augmented reality content items (e.g., corresponding toapplying Lenses or augmented reality experiences). An augmented realitycontent item may be a real-time special effect and sound that may beadded to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsrefer to modifications that may be applied to image data (e.g., videosor images). This includes real-time modifications, which modify an imageas it is captured using device sensors (e.g., one or multiple cameras)of a client device 102 and then displayed on a screen of the clientdevice 102 with the modifications. This also includes modifications tostored content, such as video clips in a gallery that may be modified.For example, in a client device 102 with access to multiple augmentedreality content items, a user can use a single video clip with multipleaugmented reality content items to see how the different augmentedreality content items will modify the stored clip. For example, multipleaugmented reality content items that apply different pseudorandommovement models can be applied to the same content by selectingdifferent augmented reality content items for the content. Similarly,real-time video capture may be used with an illustrated modification toshow how video images currently being captured by sensors of a clientdevice 102 would modify the captured data. Such data may simply bedisplayed on the screen and not stored in memory, or the contentcaptured by the device sensors may be recorded and stored in memory withor without the modifications (or both). In some systems, a previewfeature can show how different augmented reality content items will lookwithin different windows in a display at the same time. This can, forexample, enable multiple windows with different pseudorandom animationsto be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousembodiments, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshused in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various embodiments, any combinationof such modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

In other examples, other methods and algorithms suitable for facedetection can be used. For example, in some embodiments, features arelocated using a landmark, which represents a distinguishable pointpresent in most of the images under consideration. For facial landmarks,for example, the location of the left eye pupil may be used. If aninitial landmark is not identifiable (e.g., if a person has aneyepatch), secondary landmarks may be used. Such landmark identificationprocedures may be used for any such objects. In some examples, a set oflandmarks forms a shape. Shapes can be represented as vectors using thecoordinates of the points in the shape. One shape is aligned to anotherwith a similarity transform (allowing translation, scaling, androtation) that minimizes the average Euclidean distance between shapepoints. The mean shape is the mean of the aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client application 104operating on the client device 102. The transformation system operatingwithin the messaging client 104 determines the presence of a face withinthe image or video stream and provides modification icons associatedwith a computer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransform system initiates a process to convert the image of the user toreflect the selected modification icon (e.g., generate a smiling face onthe user). A modified image or video stream may be presented in agraphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transform system, may supply the user with additional interactionoptions. Such options may be based on the interface used to initiate thecontent capture and selection of a particular computer animation model(e.g., initiation from a content creator user interface). In variousembodiments, a modification may be persistent after an initial selectionof a modification icon. The user may toggle the modification on or offby tapping or otherwise selecting the face being modified by thetransformation system and store it for later viewing or browse to otherareas of the imaging application. Where multiple faces are modified bythe transformation system, the user may toggle the modification on oroff globally by tapping or selecting a single face modified anddisplayed within a graphical user interface. In some embodiments,individual faces, among a group of multiple faces, may be individuallymodified, or such modifications may be individually toggled by tappingor selecting the individual face or a series of individual facesdisplayed within the graphical user interface.

A story table 312 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 304). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 316 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 318 storesimage data associated with messages for which message data is stored inthe entity table 304. The entity table 304 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 318 and the video table 316.

The database 120 can also store 3D object data in the 3D object table314.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server114. The content of a particular message 400 is used to populate themessage table 302 stored within the database 120, accessible by themessaging server 114. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 112. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 318.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 316.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image into        within the message image payload 406, or a specific video in the        message video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 312) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the Client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 318.Similarly, values within the message video payload 408 may point to datastored within a video table 316, values stored within the messageaugmentations 412 may point to data stored in an augmentation table 310,values stored within the message story identifier 418 may point to datastored in a story table 312, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 304.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client 104. In one example,an ephemeral message 502 is viewable by a receiving user for up to amaximum of 10 seconds, depending on the amount of time that the sendinguser specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and creation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 510, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one example, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for a timeperiod specified by the group duration parameter 508. In a furtherexample, a certain ephemeral message 502 may expire, within the contextof ephemeral message group 504, based on a group participation parameter510. Note that a message duration parameter 506 may still determine theduration of time for which a particular ephemeral message 502 isdisplayed to a receiving user, even within the context of the ephemeralmessage group 504. Accordingly, the message duration parameter 506determines the duration of time that a particular ephemeral message 502is displayed to a receiving user, regardless of whether the receivinguser is viewing that ephemeral message 502 inside or outside the contextof an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 510. For example, when a sending user hasestablished a group participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message group 504 when either the groupparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a creator of a particular ephemeral message group504 may specify an indefinite group duration parameter 508. In thiscase, the expiration of the group participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message group504 will determine when the ephemeral message group 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage group 504, with a new group participation parameter 510,effectively extends the life of an ephemeral message group 504 to equalthe value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage group 504 to no longer be displayed within a user interface ofthe messaging client 104. Similarly, when the ephemeral timer system 202determines that the message duration parameter 506 for a particularephemeral message 502 has expired, the ephemeral timer system 202 causesthe messaging client 104 to no longer display an indicium (e.g., an iconor textual identification) associated with the ephemeral message 502.

FIG. 6 is a block diagram illustrating an object recognition system 124according to exemplary embodiments. The object recognition system 124 isshown as including a virtual camera system 602, a Machine learning model606, and a training database 604, all configured to communicate witheach other (e.g., via bus, shared memory, or a switch). Any one or moreof these systems may be implemented using one or more processors (e.g.,by configuring such one or more processors to perform functionsdescribed for that system and hence may include one or more processors).

Any one or more of the systems described may be implemented usinghardware alone (e.g., one or more of the processors of a machine) or acombination of hardware and software. For example, any system describedof the object recognition system 124 may physically include anarrangement of one or more of the processors (e.g., a subset of or amongthe one or more processors of the machine) configured to perform theoperations described herein for that system. As another example, anysystem of the authentication challenge issuance system 122 may includesoftware, hardware, or both, that configure an arrangement of one ormore processors (e.g., among the one or more processors of the machine)to perform the operations described herein for that system. Accordingly,different systems of the object recognition system 124 may include andconfigure different arrangements of such processors or a singlearrangement of such processors at different points in time. Moreover,any two or more systems of the object recognition system 124 may becombined into a single system, and the functions described herein for asingle system may be subdivided among multiple systems. Furthermore,according to various example embodiments, systems described herein asbeing implemented within a single machine, database, or device may bedistributed across multiple machines, databases, or devices.

In one example embodiment the virtual camera system 602 comprises aplurality of virtual cameras. The virtual camera system 602 may definedifferent arrangements (e.g., projections) for the plurality of virtualcameras. An arrangement of a camera may be defined by the position ofthe camera in relation to an object (e.g., three-dimensional object).

In some examples, the virtual camera system 602 may define a firstarrangement of the virtual cameras in which the virtual cameras aredirectly above the object. The rotation axis along which the virtualcameras are placed in relation to the object may be known and maycorrespond, to an upright orientation. Each of the virtual cameras maybe implemented using software.

In some examples, each of the virtual cameras are placed at the intervalof the angle θ (e.g., 60 degrees) around the axis and may be elevated byΦ (e.g., 30). In some examples, the elevation represents the scale ofthe object in relation to each of the virtual cameras. Thus, the virtualcamera system 602 obtains 360 degrees/60 degrees=6 views for the object.The projected view of the first viewpoint may be represented by V₁.Thus, the projected view sequence may be represented as [V₁, . . . ,V₆].

Given a three-dimensional object, different starting viewpoints (e.g.,positions of the virtual cameras) will generate different sequences. Thedifferent sequences will yield different features when fed into theMachine learning model 606 as described in further detail below. Eachobject may be associated with a predefined category (e.g., aircraft,car, chair, etc.). The features of three-dimensional objects of the samecategory may be dissimilar due to different starting viewpoints.Therefore, the virtual camera system 602 further aligns sequences of thesame class (e.g., same object category) to the same starting viewpoint.

The virtual camera system 602 further builds class-specific alignmentfunctions A_(c)(.) based on a set of permutation templates. Thealignment functions may align the sequences to a canonical viewpoint. Insome examples, a permutation function is used to process as an input,the original sequence of projected views V=[V1, . . . , V6] and asequence of indices I=[i₁, . . . , i₆] and outputs a permuted sequence:

V _(i) =[V _(i) ₁ , . . . ,V _(i) ₆ ].

The virtual camera system 602 constructs a permutation template based onthe permutation function. In some examples the permutation template isconstructed by:

P _(i)(V)=P _(i)(V,I _(i))

where I_(i) is the sequence template. The virtual camera system 602 mayuse the permutation template to construct the alignment function. Insome examples, the alignment function is constructed by:

A _(c)(V)=P _(K) _(c) _((V))(V)

where K_(c)(V) is the function determining the index of the selectedpermutation template among the set of permutation templates (P . . . ).Thus, in some examples, the alignment function A_(c)(V) is the functionof K_(c)(V). In some examples, where the orientation of the rotationaxis is known (e.g. the rotation axis is fixed), only the startingviewpoints vary between each sequence of views. For example, if thevirtual camera system 602 uses six virtual cameras, the virtual camerasystem 602 requires six permutation templates because there are only sixpossible starting view points. Therefore, six permutation templates areneeded to achieve the goal of alignment. The virtual camera system 602may specify several sequence templates I_(i) used in the permutationtemplates by:

I ₁=[1,2,3,4,5,6],

I ₂=[2,3,4,5,6,1],

-   -   . . . .

I ₆=[6,1,2,3,4,5].

Therefore, as shown above, each sequence has a different starting view.In In some examples, the virtual camera system 602 may define a secondarrangement of the virtual cameras in which the virtual cameras areplaced on the vertices of a dodecahedron surrounding the object.Therefore, the upright orientation is not given (e.g., uprightorientation is unknown). Thus, the virtual camera system 602 uses aprojection method without an upright orientation assumption. In thiscase, the viewpoints are distributed in three-dimensional space. Thevirtual camera system 602 thus obtains a sequence of 20 projected viewsV=[V₁, . . . , V₂₀].

Without upright orientation, the virtual camera system 602 constructs analignment function. In the example discussed above, the virtual camerasystem 602 requires 60 candidate permutation templates to achieve thegoal of alignment. The 60 candidate permutation templates are requiredbecause the dodecahedron has 12 faces corresponding to 12 possibleupright orientations and each upright orientation has 5 possiblestarting view points. Therefore, the virtual camera system 602 specifiesseveral sequence templates I_(i) used in the permutation templates by:

I ₁=[1,2,3,4,5, . . . 16,17,18,19,20],

I ₂=[2,3,4,5,1, . . . 17,18,19,20,16],

-   -   . . . .

I ₅=[5,1,2,3,4, . . . 20,16,17,18,19],

I ₆=[13,18,19,14,8, . . . 1,6,11,10,5],

I ₇=[18,19,14,8,13, . . . 6,11,10,5,1],

-   -   . . . .

I ₆₀=[20,19,18,17,16, . . . ,5,4,3,2,1].

Therefore, as shown above, each sequence has a different starting view.In one example embodiment the training database 604 comprises a set ofthree-dimensional objects and their associated sequence.

In one example, the machine learning model 606 comprises amachine-learning model trained to classify a three-dimensional object.In one example, the machine learning model 606 comprises of a recurrentneural network (RNN) and its variants such as Long Short Term Memorynetworks (LSTM) and Gated Recurrent Unit (GRU). In one example, themachine learning model 606 may be a sequence model. In one example, themachine learning model 606 may be a sequence-of-sequences model. Furtherdetails regarding the sequence model and the sequence-of-sequences modelare provided in relation to FIG. 8 and FIG. 9 , respectively.

FIG. 7 illustrates a machine learning model 606, according to someexample embodiments. The machine learning model 606 includes a machinelearning technique training module 704, a trained machine learningtechnique module 706, a new object image data module 708, and an objectclassification module 710.

In some implementations, some modules of machine learning model 606 beimplemented on the messaging client 104 and others may be implemented onthe application servers 112. In some implementations, all of the modulesof machine learning model 606 are implemented on messaging client 104 oron the application servers 112. In such cases, the application servers112 communicate information to messaging client 104 based on the modulesimplemented and vice versa.

The object classification training data module 702 includes a set ofthree-dimensional objects with an associated aligned sequence. The setof three-dimensional objects and their associated aligned sequences areobtained by the object classification training data module 702 from thetraining database 604.

The machine learning technique training module 704 is trained to predictthe object category of a three-dimensional object by analyzing projectedviews of the object captured by the virtual camera system 602.

Machine learning is a field of study that gives computers the ability tolearn without being explicitly programmed. Machine learning explores thestudy and construction of algorithms, also referred to herein as tools,that may learn from existing data and make predictions about new data.Such machine-learning tools operate by building a model from exampletraining data (e.g., user login attempt features and known challengeresponse labels) in order to make data-driven predictions or decisionsexpressed as outputs or assessments. Although example embodiments arepresented with respect to a few machine-learning tools, the principlespresented herein may be applied to other machine-learning tools. In someexample embodiments, different machine-learning tools may be used. Forexample, Logistic Regression (LR), Naive-Bayes, Random Forest (RF),neural networks (NN), matrix factorization, and Support Vector Machines(SVM) tools may be used for predicting a days to pending amount for agiven property.

The machine-learning algorithms utilize features for analyzing the datato generate assessments. A feature is an individual measurable propertyof a phenomenon being observed. The concept of a feature is related tothat of an explanatory variable used in statistical techniques such aslinear regression. Choosing informative, discriminating, and independentfeatures is important for effective operation of the MLP in patternrecognition, classification, and regression. Features may be ofdifferent types, such as numeric features, strings, and graphs.

The machine-learning algorithms utilize the training data to findcorrelations among the identified features that affect the outcome orassessment. In some example embodiments, the training data includeslabeled data, which is known data for one or more identified featuresand one or more outcomes, such as the days to pending amount.

Once the training data are collaged and processed, the machine learningtechnique training module 704 can be built using machine learningtechniques. Machine learning techniques train models to accurately makepredictions on data fed into the models (e.g., what was said by a userin a given utterance; whether a noun is a person, place, or thing; whatthe weather will be like tomorrow). During a learning phase, the modelsare developed against a training dataset of inputs to optimize themodels to correctly predict the output for a given input. Generally, thelearning phase may be supervised, semi-supervised, or unsupervised;indicating a decreasing level to which the “correct” outputs areprovided in correspondence to the training inputs. In a supervisedlearning phase, all of the outputs are provided to the model and themodel is directed to develop a general rule or algorithm that maps theinput to the output. In contrast, in an unsupervised learning phase, thedesired output is not provided for the inputs so that the model maydevelop its own rules to discover relationships within the trainingdataset. In a semi-supervised learning phase, an incompletely labeledtraining set is provided, with some of the outputs known and someunknown for the training dataset.

Models may be run against a training dataset for several epochs (e.g.,iterations), in which the training dataset is repeatedly fed into themodel to refine its results. For example, in a supervised learningphase, a model is developed to predict the output for a given set ofinputs, and is evaluated over several epochs to more reliably providethe output that is specified as corresponding to the given input for thegreatest number of inputs for the training dataset. In another example,for an unsupervised learning phase, a model is developed to cluster thedataset into n groups and is evaluated over several epochs as to howconsistently it places a given input into a given group and how reliablyit produces the n desired clusters across each epoch.

Once an epoch is run, the models are evaluated, and the values of theirvariables are adjusted to attempt to better refine the model in aniterative fashion. In various aspects, the evaluations are biasedagainst false negatives, biased against false positives, or evenlybiased with respect to the overall accuracy of the model. The values maybe adjusted in several ways depending on the machine learning techniqueused. For example, in a genetic or evolutionary algorithm, the valuesfor the models that are most successful in predicting the desiredoutputs are used to develop values for models to use during thesubsequent epoch, which may include random variation/mutation to provideadditional data points. One of ordinary skill in the art will befamiliar with several other machine learning algorithms that may beapplied with the present disclosure, including linear regression, randomforests, decision tree learning, neural networks, deep neural networks,and so forth.

Each model develops a rule or algorithm over several epochs by varyingthe values of one or more variables affecting the inputs to more closelymap to a desired result, but as the training dataset may be varied, andis preferably very large, perfect accuracy and precision may not beachievable. A number of epochs that make up a learning phase, therefore,may be set as a given number of trials or a fixed time/computing budget,or may be terminated before that number/budget is reached when theaccuracy of a given model is high enough or low enough or an accuracyplateau has been reached. For example, if the training phase is designedto run n epochs and produce a model with at least 95% accuracy, and sucha model is produced before the n^(th) epoch, the learning phase may endearly and use the produced model satisfying the end-goal accuracythreshold. Similarly, if a given model is inaccurate enough to satisfy arandom chance threshold (e.g., the model is only 55% accurate indetermining true/false outputs for given inputs), the learning phase forthat model may be terminated early, although other models in thelearning phase may continue training. Similarly, when a given modelcontinues to provide similar accuracy or vacillate in its results acrossmultiple epochs—having reached a performance plateau—the learning phasefor the given model may terminate before the epoch number/computingbudget is reached.

Once the learning phase is complete, the models are finalized. In someexample embodiments, models that are finalized are evaluated againsttesting criteria. In a first example, a testing dataset that includesknown outputs for its inputs is fed into the finalized models todetermine an accuracy of the model in handling data that is has not beentrained on. In a second example, a false positive rate or false negativerate may be used to evaluate the models after finalization. In a thirdexample, a delineation between data clusterings is used to select amodel that produces the clearest bounds for its clusters of data.

In some embodiments, the machine learning technique training module 704is trained to identify an object category of a three-dimensional object(e.g., identify a three-dimensional object) based on one or morefeatures (e.g., training data received from the object classificationtraining data module 702). In some embodiments the object recognitionsystem 124 may train the machine learning technique training module 704on a periodic basis (e.g., weekly, monthly, annually).

After being trained, the machine learning technique training module 704is provided to the trained machine learning technique module 706. Thetrained machine learning technique module 706 is configured to receiveprojected views of a new three-dimensional from new object image datamodule 708. In some examples the trained machine learning techniquemodule 706 is a sequence model. In some examples, the trained machinelearning technique module 706 is a sequence-of-sequences model. Forexample, the new object image data module 708 receives a sequence ofprojected views captured by the virtual camera system 602. The newobject image data module 708 instructs the trained machine learningtechnique module 706 to apply the trained machine learning technique tothe sequence of projected views provided by the new object image datamodule 708. The trained machine learning technique module 706 provides apredicted best aligned sequence based on the sequence of projected viewsprovided by the new object image data module 708.

The trained machine learning technique module 706 provides the predictedbest aligned sequence to the object classification module 710. Theobject classification module 710 uses the predicted best alignedsequence to identify the three-dimensional object.

In some examples, the trained machine learning technique module 706 isupdated using the best aligned sequence. For example, the trainedmachine learning technique module 706 is updated by calculating thefunction determining the index of the selected permutation template(e.g., best aligned sequence) among the set of permutation templates andcomputing a cross-entropy loss based on the previously determinedfunction.

FIG. 8 is an illustration of a sequence model 800. In some embodiments,the machine learning model 606 may comprise a sequence model 800. Forexample, the sequence model 800 includes an LSTM network and a GRU.

In one example embodiment, the sequence model 400 uses the GRU to obtainthe representation of each sequence of projected views V. Given asequence of projected views V of a 3D object, the sequence model firstgenerates all possible candidate aligned sequences. For each view ineach sequence P_(i)(V) 810, the sequence model generates its view-wisefeature through a convolutional neural network. The sequence model feedseach view's CNN feature 808 of each sequence P_(i)(V) 810 into a GRU 806to obtain the last hidden vector. For each permutation, the sequencemodel derives the last hidden vector. The sequence model uses all thedifferent permutations in classification of the three-dimensionalobject. In some examples, the last hidden vector is defined as:

h _(i,L) =CNN−GRU(P _(i)(V))

where L=|I| is the length of the sequence.

The representation of the sequence model is obtained by:

g _(sm)(P _(i)(V),W)=h _(i,L)

where W represents parameters of the CNN feature 808 and GRU 806.Therefore, the index of the best aligned sequence is calculated by:

${K_{c}^{sm}(\mathcal{V})} = {{\underset{i{\epsilon\lbrack{1,N}\rbrack}}{argmax}w_{c}^{T}h_{i,L}} + b_{c}}$

Each F_(c)(V) 812 is a classifier which receives features as an inputand computes a confidence score that represents that the permutationbelongs to a predefined set of categories. The softmax layer 802 is usedto compute a loss function which is used to train the neural network. Inthe sequence model 800 described above, each sequence is classifiedindependently.

FIG. 9 is an illustration of a sequence-of-sequences model 900 accordingto some example embodiments. In some embodiments, the machine learningmodel 606 may comprise a sequence-of-sequences model 900. Thesequence-of-sequences model 900 treats the sequences obtained from allthe permutation templates as another sequence. In some examples, thesequence-of-sequences model 900 exploits the order in this sequence ofsequences. The sequence of sequences may be defined as:

V _(i) ⁺ =[P ₁(P _(i)(V)), . . . P _(N)(P _(i)(V))]

In this example, the last hidden vector 908 is defined as:

h _(i,j,L) =CNN−GRU(P _(j)(P _(i)(V)),W).

The representation of the sequence of sequences is obtained myconcatenating features extracted from the sequence of sequences:

g _(sos)(P _(i)(v),W)=[h _(i,1,L) , . . . ,h _(i,N,L)]

In this example, the best aligned sequences are calculated by:

${K_{c}^{sos}(\mathcal{V})} = {{\underset{i{\epsilon\lbrack{1,N}\rbrack}}{argmax}w_{c}^{T}{g_{sos}\left( {{P_{i}(\mathcal{V})},W} \right)}} + b_{c}}$

In the sequence-of-sequences model 900 described above, each permutation904 is fed through both the CNN 906 and GRU 908 and further concatenatedinto one vector. The classification 910 is performed on the concatenatedvector. For example, the classification 810 is performed on theconcatenation of the aligned sequences. Therefore, the classification ofthe concatenation of the sequences takes into account a dependencybetween each sequence. The softmax layer 802 is used to compute a lossfunction which is used to train the neural network.

FIG. 10 is an illustration of virtual camera positions in asequence-of-sequences model, according to some example embodiments. Eachof the shaded circles 1002, 1004, 1006, 1008, 1010, and 1012 represent avirtual camera positioned over a 3D object (e.g., aircraft). Asdiscussed above, different permutation templates generate differencesequences 1014, 1016, . . . , 1018 (e.g., Sequence 1, Sequence 2, . . ., Sequence N). Each sequence has a different starting view. For example,sequence 1014 has the starting view 1002, sequence 1016 has the startingview 1012, and sequence 1018 has the staring view 1004. Therefore, themodel characterizes the order of projected views within the sequence butalso describes the order across different sequences obtained fromdifferent permutations.

FIG. 11 is a flow diagram of a method 1100 for identifying athree-dimensional model using a sequence-of-sequences model, accordingto some example embodiments. Although the described flowcharts can showoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be re-arranged. A process is terminated when itsoperations are completed. A process may correspond to a method, aprocedure, an algorithm, etc. The operations of methods may be performedin whole or in part, may be performed in conjunction with some or all ofthe operations in other methods, and may be performed by any number ofdifferent systems, such as the systems described herein, or any portionthereof, such as a processor included in any of the systems.

In operation 1102, the object recognition system 124 captures, using aplurality of virtual cameras, multiple sequences of views of athree-dimensional objection. Each sequence of the multiple sequencesrepresents a unique order of the views. Therefore, each sequence of themultiple sequences has a different starting view. In some examples,operation 1102 may be performed by the virtual camera system 602.

In operation 1104, the object recognition system 124 generates alignedsequences from the multiple sequences based on an arrangement of theplurality of virtual cameras in relation to the three-dimensionalobject. In some examples, the arrangement of the virtual camerascorresponds to an upright orientation and a known rotation axis. Theobjection recognition system 122 may concatenate the aligned sequencesinto a single vector. In some examples, operation 704 may be performedby the virtual camera system 602.

In operation 1106, the object recognition system 124 uses aconvolutional neural network to classify the three-dimensional objectbased on the aligned sequence. In some examples, the object recognitionsystem 124 classifies the three-dimensional object based on theconcatenated aligned sequences.

In operation 1108, the objection object recognition system 124identifies the three-dimensional object. In some examples, operations1106 and 1108 may be performed by the machine learning model 606.

Machine Architecture

FIG. 12 is a diagrammatic representation of the machine 1200 withinwhich instructions 1208 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1200to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1208 may cause the machine 1200to execute any one or more of the methods described herein. Theinstructions 1208 transform the general, non-programmed machine 1200into a particular machine 1200 programmed to carry out the described andillustrated functions in the manner described. The machine 1200 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1200 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1200 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1208, sequentially or otherwise,that specify actions to be taken by the machine 1200. Further, whileonly a single machine 1200 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1208 to perform any one or more of themethodologies discussed herein. The machine 1200, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1200 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1200 may include processors 1202, memory 1204, andinput/output I/O components 1238, which may be configured to communicatewith each other via a bus 1240. In an example, the processors 1202(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) Processor, a Complex Instruction Set Computing (CISC)Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), aRadio-Frequency Integrated Circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 1206and a processor 1210 that execute the instructions 1208. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.12 shows multiple processors 1202, the machine 1200 may include a singleprocessor with a single-core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 1204 includes a main memory 1212, a static memory 1214, and astorage unit 1216, both accessible to the processors 1202 via the bus1240. The main memory 1204, the static memory 1214, and storage unit1216 store the instructions 1208 embodying any one or more of themethodologies or functions described herein. The instructions 1208 mayalso reside, completely or partially, within the main memory 1212,within the static memory 1214, within machine-readable medium 1218within the storage unit 1216, within at least one of the processors 1202(e.g., within the Processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1200.

The I/O components 1238 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1238 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1238 mayinclude many other components that are not shown in FIG. 12 . In variousexamples, the I/O components 1238 may include user output components1224 and user input components 1226. The user output components 1224 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1226 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1238 may include biometriccomponents 1228, motion components 1230, environmental components 1232,or position components 1234, among a wide array of other components. Forexample, the biometric components 1228 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1230 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1232 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 3600 camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 1234 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1238 further include communication components 1236operable to couple the machine 1200 to a network 1220 or devices 1222via respective coupling or connections. For example, the communicationcomponents 1236 may include a network interface Component or anothersuitable device to interface with the network 1220. In further examples,the communication components 1236 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1222 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1236 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1236 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1236, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1212, static memory 1214, andmemory of the processors 1202) and storage unit 1216 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1208), when executedby processors 1202, cause various operations to implement the disclosedexamples.

The instructions 1208 may be transmitted or received over the network1220, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1236) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 1208may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 1222.

Software Architecture

FIG. 13 is a block diagram 1300 illustrating a software architecture1304, which can be installed on any one or more of the devices describedherein. The software architecture 1304 is supported by hardware such asa machine 1302 that includes processors 1320, memory 1326, and I/Ocomponents 1338. In this example, the software architecture 1304 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1304 includes layerssuch as an operating system 1312, libraries 1310, frameworks 1308, andapplications 1306. Operationally, the applications 1306 invoke API calls1350 through the software stack and receive messages 1352 in response tothe API calls 1350.

The operating system 1312 manages hardware resources and provides commonservices. The operating system 1312 includes, for example, a kernel1314, services 1316, and drivers 1322. The kernel 1314 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1314 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1316 canprovide other common services for the other software layers. The drivers1322 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1322 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1310 provide a common low-level infrastructure used by theapplications 1306. The libraries 1310 can include system libraries 1318(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1310 can include APIlibraries 1324 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1310 can also include a widevariety of other libraries 1328 to provide many other APIs to theapplications 1306.

The frameworks 1308 provide a common high-level infrastructure that isused by the applications 1306. For example, the frameworks 1308 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1308 canprovide a broad spectrum of other APIs that can be used by theapplications 1306, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1306 may include a home application1336, a contacts application 1330, a browser application 1332, a bookreader application 1334, a location application 1342, a mediaapplication 1344, a messaging application 1346, a game application 1348,and a broad assortment of other applications such as a third-partyapplication 1340. The applications 1306 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1306, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplication 1340 (e.g., an application developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party application1340 can invoke the API calls 1350 provided by the operating system 1312to facilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1004 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method comprising: accessing, from a pluralityof virtual cameras, multiple sequences of views of a three-dimensionalobject, each sequence of the multiple sequences representing a uniqueorder of views, wherein each sequence of views of the multiple sequencesof views has a unique starting view; generating, using one or moreprocessors, aligned sequences from the multiple sequences based on analignment function associated with the unique starting view of eachsequence of views of the multiple sequences of views; and identifyingthe identifying the three-dimensional object using the alignedsequences.
 2. The method of claim 1, wherein the plurality of virtualcameras is associated with an arrangement of the plurality of virtualcameras, wherein the arrangement of the plurality of virtual camerascorresponds to an upright orientation and a known rotation axis.
 3. Themethod of claim 1, further comprising: classifying the three-dimensionalobject using a convolutional neural network.
 4. The method of claim 1,wherein the aligned sequences are aligned to a canonical viewpoint. 5.The method of claim 3, further comprising: identifying the object usingthe classification.
 6. The method of claim 1, further comprising:concatenating the aligned sequences; and classifying thethree-dimensional object based on the concatenated aligned sequences. 7.The method of claim 3, further comprising: training the convolutionalneural network by updating the convolutional neural network with thealigned sequences.
 8. The method of claim 1, further comprising:classifying the three-dimensional object based on the aligned sequencesusing a gated recurrent unit.
 9. A system comprising: a processor; and amemory storing instructions that, when executed by the processor,configure the system to perform operations comprising: accessing, from aplurality of virtual cameras, multiple sequences of views of athree-dimensional object, each sequence of the multiple sequencesrepresenting a unique order of views, wherein each sequence of views ofthe multiple sequences of views has a unique starting view; generating,using one or more processors, aligned sequences from the multiplesequences based on an alignment function associated with the uniquestarting view of each sequence of views of the multiple sequences ofviews; and identifying the identifying the three-dimensional objectusing the aligned sequences.
 10. The system of claim 9, wherein theplurality of virtual cameras is associated with an arrangement of theplurality of virtual cameras, wherein the arrangement of the pluralityof virtual cameras corresponds to an upright orientation and a knownrotation axis.
 11. The system of claim 9, classifying thethree-dimensional object using a convolutional neural network.
 12. Thesystem of claim 9, wherein the aligned sequences are aligned to acanonical viewpoint.
 13. The system of claim 11, further comprising:identifying the object using the classification.
 14. The system of claim9, further comprising: concatenating the aligned sequences; andclassifying the three-dimensional object based on the concatenatedaligned sequences.
 15. The system of claim 11, further comprising:training the convolutional neural network by updating the convolutionalneural network with the aligned sequences.
 16. The system of claim 9,further comprising: classifying the three-dimensional object based onthe aligned sequences using a gated recurrent unit.
 17. A non-transitorycomputer-readable storage medium, the computer-readable storage mediumincluding instructions that when executed by a computer, cause thecomputer to perform operations comprising: accessing, from a pluralityof virtual cameras, multiple sequences of views of a three-dimensionalobject, each sequence of the multiple sequences representing a uniqueorder of views, wherein each sequence of views of the multiple sequencesof views has a unique starting view; generating, using one or moreprocessors, aligned sequences from the multiple sequences based on analignment function associated with the unique starting view of eachsequence of views of the multiple sequences of views; and identifyingthe identifying the three-dimensional object using the alignedsequences.
 18. The computer-readable storage medium of claim 17, whereinthe plurality of virtual cameras is associated with an arrangement ofthe plurality of virtual cameras, wherein the arrangement of theplurality of virtual cameras corresponds to an upright orientation and aknown rotation axis.
 19. The computer-readable storage medium of claim17, classifying the three-dimensional object using a convolutionalneural network.
 20. The computer-readable storage medium of claim 17,wherein the aligned sequences are aligned to a canonical viewpoint.